Sharma Cosmos DB for MongoDB Developers

Migrating to Azure Cosmos DB and Using the MongoDB API
1. ed
ISBN: 978-1-4842-3682-6
Verlag: Apress
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 218 Seiten

Learn Azure Cosmos DB and its MongoDB API with hands-on samples and advanced features such as the multi-homing API, geo-replication, custom indexing, TTL, request units (RU), consistency levels, partitioning, and much more. Each chapter explains Azure Cosmos DB's features and functionalities by comparing it to MongoDB with coding samples. 
Cosmos DB for MongoDB Developers starts with an overview of NoSQL and Azure Cosmos DB and moves on to demonstrate the difference between geo-replication of Azure Cosmos DB compared to MongoDB. Along the way you'll cover subjects including indexing, partitioning, consistency, and sizing, all of which will help you understand the concepts of read units and how this calculation is derived from an existing MongoDB's usage. 
The next part of the book shows you the process and strategies for migrating to Azure Cosmos DB. You will learn the day-to-day scenarios of using Azure Cosmos DB, its sizing strategies, and optimizing techniques for the MongoDB API. This information will help you when planning to migrate from MongoDB or if you would like to compare MongoDB to the Azure Cosmos DB MongoDB API before considering the switch.
What You Will LearnMigrate to MongoDB and understand its strategies
Develop a sample application using MongoDB's client driver
Make use of sizing best practices and performance optimization scenarios
Optimize MongoDB's partition mechanism and indexing
Who This Book Is For

MongoDB developers who wish to learn Azure Cosmos DB. It specifically caters to a technical audience, working on MongoDB. 



Manish Sharma is a senior technology evangelist at Microsoft. He has an experience of 14 years with various organizations and is primarily involved in technological enhancements.He is a certified Azure Solution Architect, Cloud Data Architect, .NET Solution Developer and PMP certified project manager. He is a regular speaker in various technical conferences organized by Microsoft (FutureDecoded, Azure Conference, specialized Webinars) & Community (GIDS, Docker etc.) for Client-Server, Cloud & Data technologies. 

Autoren/Hrsg.

Sharma, Manish

Inhaltsverzeichnis

1;Table of Contents;5
2;About the Author;10
3;About the Technical Reviewer;11
4;Acknowledgments;12
5;Introduction;13
6;Chapter 1: Why NoSQL?;15
6.1;Types of NoSQL;16
6.1.1;Key-Value Pair;16
6.1.2;Columnar;17
6.1.3;Document;17
6.1.4;Graph;18
6.2;What to Expect from NoSQL;20
6.2.1;Atomicity;20
6.2.2;Consistency;20
6.2.3;Isolation;20
6.2.4;Durability;20
6.2.5;Consistency;21
6.2.6;Availability;21
6.2.7;Partition Tolerance;21
6.2.8;Example 1: Availability;22
6.2.9;Example 2: Consistency;22
6.3;NoSQL and Cloud;22
6.3.1;IaaS;23
6.3.2;PaaS;23
6.3.3;SaaS;24
6.4;Conclusion;24
7;Chapter 2: Azure Cosmos DB Overview;25
7.1;Data Model Overview;26
7.2;Provisioning Azure Cosmos DB;27
7.3;Turnkey Global Distribution;39
7.3.1;Latency;43
7.3.2;Consistency;44
7.3.3;Throughput;44
7.3.4;Availability;44
7.3.5;Reliability;45
7.4;Protocol Support and Multimodal API;46
7.4.1;Table Storage API;46
7.4.2;SQL (DocumentDB) API;48
7.4.2.1;FROM Clause;49
7.4.2.2;WHERE Clause;49
7.4.2.3;SELECT Clause;49
7.4.2.4;ORDER BY Clause;50
7.4.2.5;Query Example;50
7.4.3;MongoDB API;52
7.4.4;Graph API;55
7.4.5;Cassandra API;62
7.5;Elastic Scale;63
7.5.1;Throughput;63
7.5.2;Storage;63
7.6;Consistency;64
7.6.1;Strong;64
7.6.1.1;Bounded Staleness;65
7.6.1.2;Session;65
7.6.1.3;Consistent Prefix;66
7.6.1.4;Eventual;67
7.6.2;Performance;68
7.7;Service Level Agreement (SLA);69
7.7.1;Availability SLA;69
7.7.2;Throughput SLA;70
7.7.3;Consistency SLA;72
7.7.4;Latency SLA;73
7.8;Conclusion;73
8;Chapter 3: Azure Cosmos DB Geo-Replication;74
8.1;Database Availability (DA);75
8.2;MongoDB Replication;75
8.2.1;Data-Bearing Nodes;75
8.2.2;Arbiter Nodes;77
8.3;Azure Cosmos DB Replication;80
8.4;Auto-Shifting Geo APIs;85
8.5;Consistency and Global Distribution;91
8.6;Conclusion;92
9;Chapter 4: Indexing;93
9.1;Indexing in MongoDB;93
9.1.1;Single Field Index;94
9.1.1.1;Query Using an Index;95
9.1.1.2;Query Not Using an Index;97
9.1.2;Compound Index;99
9.1.3;Multikey Index;100
9.1.4;Geospatial Index;100
9.1.5;Text Index;103
9.1.6;Hashed Index;105
9.2;Indexing in Azure Cosmos DB;105
9.2.1;TTL Indexes;107
9.2.2;Array Indexes;108
9.2.3;Sparse Indexes;109
9.2.4;Unique Indexes;109
9.3;Custom Indexing;110
9.3.1;Indexing Modes;111
9.3.2;Indexing Paths;116
9.3.3;Index Kinds;117
9.3.3.1;Hash Indexes;117
9.3.3.2;Range Indexes;117
9.3.3.3;Geospatial Indexes;117
9.3.4;Index Precision;120
9.3.5;Data Types;120
9.4;Conclusion;120
10;Chapter 5: Partitioning;121
10.1;Sharding;121
10.2;Partitioning in Azure Cosmos DB;125
10.3;Optimizations;134
10.4;Selecting a Partition Key;138
10.4.1;Use Case;138
10.4.2;Evaluate Every Field to Be a Potential Partition Key;139
10.4.3;Selection of the Partition Key;140
10.5;Conclusion;147
11;Chapter 6: Consistency;148
11.1;Consistency in Distributed Databases;148
11.2;Consistency in MongoDB;151
11.3;Consistency in Azure Cosmos DB;153
11.3.1;Consistent Reads/Writes;153
11.3.1.1;Strong Consistency;153
11.3.1.2;Bounded Staleness;156
11.3.1.3;Session;158
11.3.2;High Throughput;159
11.3.2.1;Consistent Prefix;160
11.3.2.2;Eventual;161
11.4;Conclusion;164
12;Chapter 7: Sizing;166
12.1;Request Units (RUs);166
12.2;Allocation of RUs;167
12.3;Calculating RUs;170
12.4;Optimizing RU Consumption;175
12.4.1;Document Size and Complexity;175
12.4.2;Data Consistency;179
12.4.3;Indexing;182
12.4.4;Query Patterns;187
12.5;Conclusion;188
13;Chapter 8: Migrating to Azure Cosmos DB–MongoDB API;189
13.1;Migration Strategies;189
13.1.1;mongoexport and mongoimport;190
13.1.1.1;For Linux;190
13.1.2;For Windows mongodump/mongorestore;191
13.1.2.1;For Linux;191
13.1.2.2;For Windows;192
13.1.2.3;BulkExecutor;193
13.2;Application Switch;194
13.3;Optimization;196
13.4;Conclusion;198
14;Chapter 9: Azure Cosmos DB–MongoDB API Advanced Services;200
14.1;Aggregation Pipeline;200
14.2;Spark Connector;205
14.3;Conclusion;213
15;Index;214